Summary

• Interested in finding Long Period Variables (LPVs).
• Built a classifier to sort candidates into:
  • LPVs, non-LPVs, and non-variables.
• Used upsampling and downsampling to create a balanced training set for use with a gradient-boosted decision tree classifier.
• Identified 159,696 LPVs from the PGIR survey; 73,346 newly identified.

Data

• Palomar Gattini-IR (PGIR) survey
• September 2018 – July 2021 (\(\approx\) 1400d)
• Near-Infrared J band

Method

1. Build an initial bonafide training set (\(n = 1344\))
2. Extend training set by taking a subsample of the full survey and classifying these.
3. Train the gradient-boosted decision tree on the extended training dataset.
4. Apply classifier to subset of PGIR survey (\(N = 35\textrm{M}\)).
5. Compare the labelled result with subset of GAIA DR3 database of LPVs (\(N = 150,195\)).

Features

Class Imbalance

• bonafide training set:
  • 1265 LPVs vs. 79 non-LPVs
• ADASYN to upsample minority class, allKNN to downsample majority class
• extended training set:
  • 2335 LPVs
  • 444 Type-II LPVs
  • 166 non-LPVs
  • 1332 non-variables

Training Set Confusion Matrix

Variable Importance

Comparison with GAIA

Comments

• GPs used for interpolation but no details on hyperparameters.
• Selection bias when choosing what subset of survey to build training set from.
  • von Neumann score used to choose \(\Rightarrow\) important variable.
• Still have some class imbalance!
• Why would colour be a good classification feature?
• Interesting approach of creating new label after looking at results.